1. Field of the Invention
The present invention relates to a method and apparatus for classifying a mood of music at high speed, and more particularly, to a method and apparatus for extracting a Modified Discrete Cosine Transformation (MDCT)-based timbre feature and tempo feature from a compressed domain of a music file and classifying the mood of the music file based on the extracted timbre feature and the extracted tempo feature.
2. Description of Related Art
In conventional methods of automatically detecting the mood of music, timbre features such as a spectral shape feature, a spectral contrast feature and a rhythm feature such as an intensity feature, average strength, average regularity, and average tempo are extracted from and used for classifying the mood of a music file. Also, in some of the conventional methods of automatically detecting the mood of the music, the mood is classified into four different moods by a hierarchical structure using Gaussian Mixture Model (GMM). However, in the conventional method of automatically detecting the mood of the music, since features of the music have to be extracted from a decompressed domain in which an encoded music file is decoded, extraction is low and, as a result, detection is slowed. Also, in some of the conventional method of automatically detecting the mood of the music, the mood of the music is classified by modeling a mood class that is simply defined and regardless of genres, thereby generating many classification errors.
Conversely, in a conventional music recommendation system, high-capacity music files stored in a hard disk driver (HDD) are classified according to a taste of a user. Specifically, in the conventional music recommendation system, for example, 249 tunes are stored, 10 tunes are presented for each mood designated by the user, and the user performs feedback as fit/unfit for each tune, thereby performing a selection of music for each mood classified into bright, exciting, quiet, sad, and healing. However, in this conventional music recommendation system, extraction speeds are slow because features of the music have to be extracted from a decompressed domain in which an encoded music file is decoded. Also, a difficulty in the conventional music recommendation system is that feedback needs to be performed more than 18 times so that tunes desired by the user can be selected at an 85% selection rate.
As described above, in the conventional music mood classification methods, slow extraction speeds exist because a decoding process of converting an encoded music file such as MP3 into PCM data is required in order to extract the features of the music file, such as timbre, tempo, and intensity from a decompressed domain.
Also, in the conventional music mood classification method, a many classification errors are generated, caused by mood classes defined regardless of genres.
In addition, in the conventional music mood classification method and apparatus, a method of displaying a result of classifying a plurality of music files is overlooked. Specifically, in the conventional music mood classification method and apparatus, for example, when 1,000 tunes are classified in moods, an exciting tune is selected because a user wants to hear the exciting tune, and exciting tunes are played in the same order every time, thereby making the user perceive the apparatus is unsophisticated.
Accordingly, as a method of solving the perception of the apparatus is unsophisticated, a method of enabling an arrangement order to be random and a music mood classification method of arranging music files in an order of high reliability of classification such that the reliability of the apparatus, specifically, a result of mood classification, is recognized to be high.